During a magnetic resonance (MR) imaging session, the patient is placed inside a strong magnetic field generated by a large magnet. Magnetized protons within the patient, such as hydrogen atoms, align with the magnetic field produced by the magnet. A particular slice of the patient is exposed to radio waves that create an oscillating magnetic field perpendicular to a main magnetic field. The slices can be taken in any plane chosen by the physician or technician performing the imaging session. The protons in the patient's body first absorb the radio waves and then emit the waves by moving out of alignment with the field. As the protons return to their original state (before excitation), diagnostic images based upon the waves emitted by the patient's body are created. MR image slices can be reconstructed to provide a 3-dimensional picture of the region of interest. In the reconstructed image, parts of the body that produce a high signal are displayed as white in an MR image, while those with the lowest signals are displayed as black. Other body parts that have varying signal intensities between high and low are displayed as some shade of gray.
Variations in image acquisition parameters affect various qualities of the reconstructed images generated. For example, in dual echo MR imaging, two images are generated for each slice, where the first and second images are generated using different image acquisition parameters such as different repetition times (TR) and/or echo times (TE). Depending on the parameters used for each image generated, one image may show better contrast between different certain tissues of interest, such as solid tissues, e.g., white and gray matter in the brain, while another image may show better contrast between other tissues of interest, such as fluids and solids, e.g., cerebrospinal fluid (CSF) and gray matter.
Image segmentation is useful for dividing an image into meaningful regions, such as for distinguishing between different tissue types. Typically, each voxel of the image is examined and assigned a label that associates it with a region, i.e., a tissue type for medical images, based on properties of the voxel, its neighbors or similarity to other voxels assigned to a region. The classification of voxels into tissue types allows for a highly intelligible display of information by the image, such as for educational or diagnostic purposes, for providing a guide during surgery, for defining boundaries of structures, for determining volumes of structures, and for monitoring changes in structural volumes. Multiple images taken at the same time or at different times may be used to study the same region of interest. Integration of information displayed in the multiple images is enhanced using a technique known as registration for correlating voxels of the multiple images.
One method of segmentation known in the art is cluster identification in a 2-D scatter plot (also known as a feature space), which includes using Cartesian coordinates mapping the intensity of sampled voxels from a first image obtained using a first echo (x-axis) vs. the intensity of sample voxels from a second image obtained using a second echo (y-axis). Clusters of voxels in the mapping, signifying voxels having similar properties and likely to correspond to the same tissue type, are identified and assigned a color. The clusters may be formed of a tight grouping or a spread out diffuse grouping of mapped points. Corresponding voxels in the first and second image (or other registered images) are also assigned the color for a displaying the cluster identification segmentation within the images. Further processing may be performed based on the colored images, such as identification of a specific structure within a tissue type, and volume measurement of a structure.
However, typically the cluster identification is performed with manual intervention using a qualitative analysis, which is time and resource consuming, and generates inconsistencies and subjectivity.
Accordingly, there is a need for a system and method for a quantitative analysis of a 2-D scatter plot in which manual intervention is minimized.